Ecole d'ingénieur et centre de recherche en Sciences du numérique

Disease progression modeling and prediction through random effect Gaussian processes and time transformation

Lorenzi, Marco; Filippone, Maurizio; Alexander, Daniel C; Ourselin, Sebastien

Submitted to ArXiV; January 7, 2017

The development of statistical approaches for the joint modelling of the temporal changes of imaging, biochemical, and clinical biomarkers is of paramount importance for improving the understanding of neurodegenerative disorders, and for providing a reference for the prediction and quantification of the pathology in unseen individuals. Nonetheless, the use of disease progression models for probabilistic predictions still requires investigation, for example for accounting for missing observations in clinical data, and for accurate uncertainty quantification. We tackle this problem by proposing a novel Gaussian process-based method for the joint modeling of imaging and clinical biomarker progressions from time series of individual observations. The model is formulated to account for individual random effects and time reparameterization, allowing non-parametric estimates of the biomarker evolution, as well as high flexibility in specifying correlation structure, and time transformation models. Thanks to the Bayesian formulation, the model naturally accounts for missing data, and allows for uncertainty quantification in the estimate of evolutions, as well as for probabilistic prediction of disease staging in unseen patients. The experimental results show that the proposed model provides a biologically plausible description of the evolution of Alzheimer’s pathology across the whole disease time-span as well as remarkable predictive performance when tested on a large clinical cohort with missing observations.

Bibtex

Titre:Disease progression modeling and prediction through random effect Gaussian processes and time transformation
Type:Conférence
Langue:English
Ville:
Date:
Département:Data Science
Eurecom ref:5114
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted to ArXiV; January 7, 2017 and is available at :
Bibtex: @inproceedings{EURECOM+5114, year = {2017}, title = {{D}isease progression modeling and prediction through random effect {G}aussian processes and time transformation}, author = {{L}orenzi, {M}arco and {F}ilippone, {M}aurizio and {A}lexander, {D}aniel {C} and {O}urselin, {S}ebastien}, booktitle = {{S}ubmitted to {A}r{X}i{V}; {J}anuary 7, 2017}, address = {}, month = {01}, url = {http://www.eurecom.fr/publication/5114} }
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